trainer.py 文件源码

def __init__(self, config, models):
        model = models[0]
        assert isinstance(model, Model)
        self.config = config
        self.model = model
        self.opt = tf.train.AdadeltaOptimizer(config.init_lr)
        self.var_list = model.get_var_list()
        self.global_step = model.get_global_step()
        self.summary = model.summary
        self.models = models
        losses = []
        grads_list = []
        for gpu_idx, model in enumerate(models):
            with tf.name_scope("grads_{}".format(gpu_idx)), tf.device("/{}:{}".format(config.device_type, gpu_idx)):
                loss = model.get_loss()
                grads = self.opt.compute_gradients(loss, var_list=self.var_list)
                losses.append(loss)
                grads_list.append(grads)

        self.loss = tf.add_n(losses)/len(losses)
        self.grads = average_gradients(grads_list)
        self.train_op = self.opt.apply_gradients(self.grads, global_step=self.global_step)